Problems with DDP + hydra

[Lukas]

There have been numerous issues about using DDP with hydra:
#231 #289 #229 #226 #194 #352

Current state of things is well described here:
facebookresearch/hydra#2070

tl;dr:
You should be good when using current lightning-hydra-template with ddp_spawn:

# run ddp_spawn on 4 GPUs
python train.py trainer.strategy=ddp_spawn trainer.accelerator=gpu trainer.devices=4

# simulate ddp_spawn on CPU on 4 processes (for testing)
python train.py trainer.strategy=ddp_spawn trainer.accelerator=cpu trainer.devices=4

This works correctly with normal runs as well as multiruns as far as I'm aware.

(ddp_spawn works a bit slower than normal ddp and should be run with datamodule.num_workers=0 only)

Normal ddp computes correctly but generates multiple output directories.

I have not tested what happens when using SLURM.

For now, I don't see anything that can be done on the template part to fix this. This might change with future hydra releases.

Update (April 2023):
Nornal DDP seems to be working correctly with current lightning release (2.0.2). There are no longer multiple output directories.

[bwdeng20]

Thanks for the summary👍🏻. Looking forward to future fixs.

[Joseph Turian]

@ashleve Can you explain why ddp_spawn is better than ddp? (I skimmed the issues but couldn't grok it.) I understand from the lightning docs that spawn is slower than ddp

[Lukas]

@turian ddp_spawn is not better but it's the only ddp mode that works correctly with hydra right now.

As I mentioned, normal DDP generates multiple unwanted files. This is due to the fact that ddp launches a new process for each GPU, which doesn't go well with the way hydra creates different output dir each time a program is launched.

The problem doesn't exist with ddp_spawn which uses a different strategy of launching new GPU processes.

[Joseph Turian]

@ashleve Just curious because I am using hydra + DDP in a current project. How would I be able to detect if this issue is occurring for me? What evidence should I look for? Thank you for the tip

[Lukas]

@turian There will be more output directories as explained in facebookresearch/hydra#2070

Just to make this clear, normal ddp actually computes correctly in hydra single run mode, but you will have multiple output directories with .hydra files

[Joseph Turian]

@ashleve woof that's gross. If you have a good fix, we might considering seeing if we can push it upstream to lightning.

[Joseph Turian]

@ashleve Lightning team appears to be working on this issue?

Lightning-AI/pytorch-lightning#11617 (comment)

I've been lightly commenting in that PR

[Fabian-Robert Stöter]

@turian @ashleve what worked for me as a workaround is making the experiment dirs static (especially for multiruns/sweeps)

e.g.

hydra:
  run:
    dir: experiments/${your_run_name}
  job:
    chdir: False
  sweep:
    dir: experiments/${your_run_name}
    subdir: ""

callbacks:
  cp:
    dirpath: "experiments/${your_run_name}/checkpoints"
    filename: "${your_run_name}_best_step{step:08d}"

you would loose the ability to have a separate directory for sweeper results, but you could override this specifically for optuna optimization sweeps if you like

[Xujin Chris Liu]

It looks like the pr has been merged into lightning main!

[AiEson]

Has this issue been fixed? Can I use ddp for training instead of ddp_spawn？

[Beau Bo]

@ashleve Was this fixed by the newest release to use PyTorch 2.0 and PyTorch Lightning 2.0? Thank you for your time.

[Lukas]

@AiEson @libokj It seems like the issue with ddp is indeed fixed.

I've checked on multi-gpu instance and at first glance, everything seems to be computed correctly with no redundant logging directories. Although issues with ddp are often hard to spot so let me know if you encounter some problems.

For reference, here are some of the commands I've checked:

python src/train.py trainer.accelerator=gpu trainer.strategy=ddp trainer.devices=2 
python src/train.py trainer.accelerator=gpu trainer.strategy=ddp trainer.devices=2  data.num_workers=8
python src/train.py trainer.accelerator=gpu trainer.strategy=ddp_spawn trainer.devices=2
python src/train.py trainer.accelerator=cpu trainer.strategy=ddp_spawn trainer.devices=2 

I made the appropriate changes to ddp config #571

[Beau Bo]

I really appreciate your update! Thank you again.

[Tomakko]

Hi,

when using ddp i still end up with two, sometimes three, directories per sweep under logs/train/multirun/.

I am executing python src/train.py -m trainer=ddp trainer.devices=4 data.batch_size=32,64 model.optimizer.lr=0.001,0.004.

My lightning version is 2.0.4.

Is there anything i am missing? Thanks!

[lyp]

There are two files (train.log and train_ddp_process_1.log) and one folder (.hydra) are produced in the ROOT_DIR

[Beau Bo]

run:
  dir: ${paths.log_dir}/${job_name}/runs/${now:%Y-%m-%d}_${now:%H-%M-%S}_${tags}
sweep:
  dir: ${paths.log_dir}/${job_name}/multiruns/${now:%Y-%m-%d}_${now:%H-%M-%S}_${tags}
  # Sanitize override_dirname by replacing '/' with ',' to avoid unintended subdirectory creation
  subdir: ${eval:'"${hydra.job.override_dirname}".replace("/", ".")'}

job_logging:
  handlers:
    file:
      filename: ${hydra:runtime.output_dir}/job.log

With my current configuration for hydra, multiple folders will be created for the same ddp job, one for each ddp process with a slight time difference, e.g. 2023-09-09_02-00-58_tags and 2023-09-09_02-01-04_tags. The first created folder will contain all the intended logs but the other redundant folders will contain a job.log for its corresponding ddp process. Any advice on this? @ashleve

[Hovnatan Karapetyan]

@libokj what if you try

if __name__ == "__main__":
    if os.environ.get("LOCAL_RANK", 0) != 0:
        sys.argv.extend(
            [
                "hydra/hydra_logging=disabled",
                "hydra/job_logging=disabled",
           ]
        )
main()

wher main() is your hydra app